Image related problems are often observed in imaging/visualization systems, such as printers and projectors. Their diagnosis requires the capability to precisely describe the visual artifacts that are created by such system and/or equipment problems. However, characterizing visual artifacts is difficult, particularly because the characterization is often subjective, hence descriptions generated by human observers are largely qualitative, rather than quantitative. In most instances, a precise image quality description can only be provided by a knowledgeable, experienced technician who is familiar with the equipment. Conversely, automated image quality characterization tools often have difficulty in generating descriptors matching the evaluation provided by what is termed the human visual system, i.e. a visual evaluation from a skilled technician or other person based on his/her visual assessment of the artifact.
Much work has been done on image quality characterization. For instance, Xerox's Image Quality Analysis Facility (IQAF) offers a suite of tools for analyzing image artifacts, ranging from simple banding in solid color test patterns to complicated analysis in customer images. Even so, there remains a need for more precise image artifact analysis to efficiently identify artifacts and use this information to identify the source of the artifact so that the problem may be addressed.